PDF《SUPPORTING INFORMATION》

S\1? SUPPORTING INFORMATION Improving Label-Free Quantitative Proteomics Strategies by Distributing Shared Peptides and Stabilizing Variance Ying Zhang1? , Zhihui Wen1? , Michael P.

Washburn1,

2 , and Laurence Florens1*

1 Stowers Institute for Medical Research,

1000 E. 50th Street, Kansas City, Missouri 64110, USA

2 Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center,

3901 Rainbow Boulevard, Kansas City, Kansas 66160, USA ABSTRACT: In a previous study, we demonstrated that spectral counts based label-free proteomic quantitation could be improved by distributing peptides shared between multiple proteins. Here, we compare four quantitative proteomic approaches;

namely the Normalized Spectral Abundance Factor (NSAF), the Normalized Area Abundance Factor (NAAF), Normalized Parent Ion Intensity Abundance Factor (NIAF), and the Normalized Fragment Ion Intensity Abundance Factor (NFAF). We demonstrate that label-free proteomic quantitation methods based on chromatographic peak area (NAAF), parent ion intensity in MS1 (NIAF), and fragment ion intensity (NFAF) are also improved when shared peptides are distributed based on peptides unique to each isoform. To stabilize the variance inherent to label-free proteomic quantitation datasets, we use cyclic-locally weighted scatter plot smoothing (LOWESS) and linear regression normalization (LRN). Again, all four methods are improved when cyclic-LOWESS and LRN are applied to reduce variation. Finally, we demonstrate that absolute quantitative values may be derived from label-free parameters such as spectral counts, chromatographic peak area, and ion intensity when using spiked-in proteins of known amounts to generate standard curves. Table of Content: Figure S1. Linear regression between yNXAF values and known albumin amounts. S2 Supporting Table 1: Label-free features measured for albumin isoforms digested with trypsin. S3-S16 Supporting Table 2: Statistical analyses. (A)Statistical analysis of the linear correlations between log2(u/dNXAF) and log2(Amount) (B) Statistical analysis of the standard deviations of dNXAF values measured for the

6 albumins (C) Statistical analysis of the slopes of the linear regressions between Log2(u/dNXAF) and Log2(Amount) (D)Statistical analysis of the reproducibility of dNXAF values measured between technical replicates (E) Statistical analysis of the effect of cyclic-LOWESS and LNR normalization on dNXAF values S17-S18 S19-S20 S21 S22-S23 S24-S32 Supporting Table 3: Absolute quantitation of proteins based on linear regression through standards of known amounts. (A)S. cerevisiae (B) E. coli S33-S52 S53-S71 ? Zhang_dNXAF_FigS1? S\2? Figure S1. Linear regression between yNXAF values and known albumin amounts. Log2-transformed yNXAF values (see Table 1) are plotted as a function of log2-transformed albumin amounts in picomoles (Table S1). (A), (B), (C), and (D) Panels report the linear regression between albumin amounts and nNXAF, uNXAF, dNXAF, and dNXAFcLL (obtained after cyclic-LOWESS and LRN), respectively. ?

14 PRINTED PAGES Zhang_dNXAF_TableS1 Description Pig Albumin Mouse Albumin Rabbit Albumin Human Albumin Rat Albumin Bovine Albumin Locus gi|51235682|gb|AA T98610.1| gi|19353306|gb|AA H24643.1| gi|126723746|ref|N P_001075813.1| gi|178344|gb|AAA9 8797.1| gi|158138568|ref|N P_599153.2| gi|162648|gb|AAA5 1411.1| pI 6.5 6.1 6.2 6.3 6.5 6.2 MW

69692 68693

68910 69367

68759 69294 L (length)

607 608

608 609

608 607 uL (Ti)

414 361

489 460

343 463 sL (Ti)

193 247

119 149